Integrating remote testing and machine learning to identify markers of cerebellar ataxia at home.

BACKGROUND

In-person assessments face accessibility, scalability, and geographic diversity challenges, especially for rare diseases. Additionally, Cerebellar Ataxia (CA) non-motor symptoms(NMS) are often overlooked. We aimed to address these gaps by leveraging the Internet and machine-learning.

METHODS

In a bi-center study, we assessed 100 participants: 30 CA, 45 neurotypically healthy(NH), and 25 Parkinson's disease(PD), recruited from 57 geographical locations across two countries. We evaluated multiple domains-cognition, anxiety, depression, social support, and personality-using accessible online tools. We applied leave-one-out cross-validation and feature importance analysis to examine the machine-learning model's ability to distinguish between groups and identify the most sensitive and specific CA predictors.

RESULTS

Machine-learning models trained on these remote non-motor features alone, yield AUCs of 0.74/0.76(CA vs. NH) and 0.78/0.79 (CA vs. PD) using leave-one-out cross-validation, demonstrating classification power exceeding 20%.

CONCLUSION

These findings highlight the value of integrating digital-health technologies and machine-learning models for CA NMS evaluation, potentially serving as scalable digital-markers.